Automatic Classification of Fruit Defects based on Co-Occurrence Matrix and Neural Networks

• Autorzy: Capizzi , Lo Sciuto , Napoli , Tramontana , Woźniak
• Języki publikacji: EN

Abstrakty

EN

Nowadays the effective and fast detection of fruit defects is one of the main concerns for fruit selling companies. This paper presents a new approach that classifies fruit surface defects in color and texture using Radial Basis Probabilistic Neural Networks (RBPNN). The texture and gray features of defect area are extracted by computing a gray level co-occurrence matrix and then defect areas are classified by the applied RBPNN solution.(original abstract)

Słowa kluczowe

EN

Neural networks

PL

Sieci neuronowe

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2015
• Tom: 5
• Strony: 861--867

Twórcy

autor

Capizzi

• University of Catania, Italy

autor

Lo Sciuto

• University of Roma Tre, Italy

autor

Napoli

• University of Catania, Italy

autor

Tramontana

• University of Catania, Italy

autor

Woźniak

• Silesian University of Technology, Gliwice, Poland

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Identyfikatory

DOI

10.15439/2015F258